1 day ago
Responsibilities
- Serve as the technical lead for an ML-focused team and set its technical direction.
- Design, build, and scale ML systems for personalization, ranking, search, and advertising use cases.
- Own architecture for model training, evaluation, deployment, and serving.
- Translate product needs into scalable technical solutions in ambiguous problem spaces.
- Lead design reviews and make tradeoffs involving latency, reliability, experimentation, and maintainability.
- Partner with product, data, and engineering stakeholders to deliver measurable business impact.
- Mentor engineers through hands-on technical guidance and feedback.
- Use AI and LLM tools for prototyping, code iteration, architecture validation, and potential LLM-powered features.
Requirements
- At least 6 years of industry experience in machine learning or software engineering.
- Demonstrated ownership of production ML systems operating at scale.
- Experience building and scaling ML systems in personalization, relevance, search, or ad technology.
- Strong hands-on expertise in distributed systems, data pipelines, and ML infrastructure.
- Experience deploying and operating ML models at consumer scale.
- Experience owning complex technical initiatives, applying systems design, and articulating technical tradeoffs.
- Experience mentoring engineers and influencing technical standards.
- Ability to operate in ambiguous environments and drive projects to completion.
- Bachelor’s degree in Computer Science, Engineering, or a related technical field.
- Preferred: familiarity with LLMs, real-time or streaming ML, experimentation frameworks, model performance measurement, and real-time serving systems.
Benefits
- Full-time role available from a US office or remotely in the United States or any Canadian province excluding Quebec.
- Canadian employment, where applicable, is through Fetch’s Canadian Employer of Record.
- Benefits for Canada-based employees may differ to comply with applicable laws.
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About Fetch
Fetch builds a consumer rewards app where users earn points by scanning receipts and redeem them for gift cards, while brands use its SKU-level, retail-agnostic purchase data for targeted, outcomes-based advertising and measurement. The privately held company, founded in 2013, processes over 13 million receipts daily, creating visibility into more than $200 billion in gross merchandise value. Its platform has awarded over $1 billion in points and operates a hybrid-remote workplace.